Proportional Integral Controller Enhanced Research Articles

The Impact of Denial-of-Service Attacks and Queue Management Algorithms on Cellular Networks

In today's digital landscape, Distributed Denial of Service (DDoS) attacks stand out as a formidable threat to organisations all over the world. As known technology gradually advances and the proliferation of mobile devices, cellular network operators face pressure to fortify their infrastructure against these risks. DDoS incursions into Cellular Long-Term Evolution (LTE) networks can wreak havoc, elevate packet loss, and suboptimal network performance. Managing the surges in traffic that afflict LTE networks is of paramount importance. Queue management algorithms emerge as a viable solution to wrest control over congestion at the Radio Link Control (RLC) layer within LTE networks. These algorithms work proactively, anticipating, and mitigating congestion by curtailing data transfer rates and fortifying defences against potential DDoS onslaughts. In the paper, we delve into a range of queue management methods Drop-Tail, Random Early Detection (RED), Controlled Delay (CoDel), Proportional Integral Controller Enhanced (PIE), and Packet Limited First In, First Out queue (pFIFO). Our rigorous evaluation of these queue management algorithms hinges on a multifaceted assessment that encompasses vital performance parameters. We gauge the LTE network's resilience against DDoS incursions, measuring performance based on end-to-end delay, throughput, packet delivery rate (PDF), and fairness index values. The crucible for this evaluation is none other than the NS3 simulator, a trusted platform for testing and analysis. The outcomes of our simulations provide illuminating insights. CoDel, RED, PIE, pFIFO, and Drop-Tail algorithms emerge as top performers in succession. These findings underscore the critical role of advanced queue management algorithms in fortifying LTE networks against DDoS attacks, offering robust defences and resilient network performance.

Acknowledge-Based Non-Congestion Estimation: An Indirect Queue Management Approach for Concurrent TCP and UDP-Like Flows

This paper presents a new approach for indirect Active Queue Management (indirect AQM) technique called Acknowledge-based Non-Congestion Estimation (ANCE), which employs end-to-end queue management along a network instead to use Explicit Congestion Notification (ECN) bit or to drop packets in the queue. The ANCE performance was compared with Random Early Detection (RED), Control Delay (CoDel), Proportional Integral controller Enhanced (PIE), Explicit Non-Congestion Notification (ENCN), TCP-Jersey and E-DCTCP schemes in a daisychain and in a dumbbell cenario, with TCP flows and UDP-like Networked Control Systems (NCS) flow sharing the same network topology. On the other hand, this paper presents a method for modeling, simulation and verification of communication systems and NCS, using UPPAAL software tool, on which, all network components (channels, routers, transmitters, receivers, plants, and Controllers) were modeled using timed automata making easy a formal verification of the whole modeled system. Simulations and statistical verification show that despite using fewer resources (since ANCE does not need the ECN bit) ANCE presents a very close performance to ENCN overcoming Drop Tail, RED, CoDel, PIE and E-DCTCP in terms of Integral Time Absolute Error (ITAE) for NCS and fairness for TCP flows. ANCE also attains better performance than RED, PIE, TCP-Jersey and E-DCTCP in terms of throughput for TCP flows.

Inductanceless high order low frequency filters for medical applications

<p>In this paper, a designed circuit used for low-frequency filters is implemented and realized the filter is based on frequency-dependent negative resistance (FDNR) as an inductor simulator to substitute the traditional inductance, which is heavy and high cost due to the coil material manufacturing and size area. The simulator is based on an active operation amplifier or operation transconductance amplifier (OTA) that is easy to build in an integrated circuit with a minimum number of components. The third and higher-order Butterworth filter is simulated at low frequency for low pass filter to use in medical instruments and low-frequency applications. The designed circuit is compared with the traditional proportional integral controller enhanced (PIE) and T section ordinary filter. The results with magnitude and phase response were compared and an acceptable result is obtained. The filter can be used for general applications such as medical and other low-frequency filters needed.</p>

Revisiting design choices in queue disciplines: The PIE case

Bloated buffers in the Internet add significant queuing delays and have a direct impact on the user perceived latency. There has been an active interest in addressing the problem of rising queue delays by designing easy-to-deploy and efficient Active Queue Management (AQM) algorithms for bottleneck devices. The real deployment of AQM algorithms is a complex task because the efficiency of every algorithm depends on appropriate setting of its parameters. Hence, the design of AQM algorithms is usually entrusted on simulation environments where it is relatively straightforward to evaluate the algorithms with different parameter configurations. Unfortunately, several factors that affect the efficiency of AQM algorithms in real deployment do not manifest during simulations, and therefore, lead to inefficient design of the AQM algorithm. In this paper, we revisit the design considerations of Proportional Integral controller Enhanced (PIE), an algorithm widely considered for network deployment, and extensively evaluate its performance using a Linux based testbed. Our experimental study reveals some performance anomalies in certain circumstances and we prove that they can be attributed to a specific design choice of PIE, namely the use of the estimated departure rate to compute the expected queuing delay. Therefore, we designed an alternative approach based on packet timestamps, implemented it in the Linux kernel and proved its effectiveness through an experimental campaign.

Adaptive Chunklets and AQM for Higher-Performance Content Streaming

Commercial streaming services such as Netflix and YouTube use proprietary HTTP-based adaptive streaming (HAS) techniques to deliver content to consumers worldwide. MPEG recently developed Dynamic Adaptive Streaming over HTTP (DASH) as a unifying standard for HAS-based streaming. In DASH systems, streaming clients employ adaptive bitrate (ABR) algorithms to maximise user Quality of Experience (QoE) under variable network conditions. In a typical Internet-enabled home, video streams have to compete with diverse application flows for the last-mile Internet Service Provider (ISP) bottleneck capacity. Under such circumstances, ABR algorithms will only act upon the fraction of the network capacity that is available, leading to possible QoE degradation. We have previously explored chunklets as an approach orthogonal to ABR algorithms, which uses parallel connections for intra-video chunk retrieval. Chunklets effectively make more bandwidth available for ABR algorithms in the presence of cross-traffic, especially in environments where Active Queue Management (AQM) schemes such as Proportional Integral controller Enhanced (PIE) and FlowQueue-Controlled Delay (FQ-CoDel) are deployed. However, chunklets consume valuable server/middlebox resources which typically handle hundreds of thousands of requests/connections per second. In this article, we propose ‘adaptive chunklets’ -- a novel chunklet enhancement that dynamically tunes the number of concurrent connections. We demonstrate that the combination of adaptive chunklets and FQ-CoDel is the most effective strategy. Our experiments show that adaptive chunklets can reduce the number of connections by almost 30% and consume almost 8% less bandwidth than fixed chunklets while providing the same QoE.

Minstrel PIE: Curtailing queue delay in unresponsive traffic environments

Active Queue Management (AQM) algorithms aim to maintain a proper trade-off between queue delay and bottleneck link utilization. However, it is often noticed that this trade-off is not achieved convincingly when unresponsive UDP flows coexist with responsive TCP flows. This paper proposes an extension to Proportional Integral controller Enhanced (PIE) algorithm called Minstrel PIE, which adapts the reference queue delay to improve the trade-off between queue delay and link utilization when unresponsive flows share the same bottleneck queue as responsive flows. Extensive evaluations through simulations and real time experiments demonstrate that Minstrel PIE improves the performance of PIE in the presence of unresponsive flows, and delivers similar performance otherwise. Moreover, the Minstrel PIE algorithm does not introduce new knobs to improve the performance of PIE and hence, can be easily deployed without any additional complexity.

Using Active Queue Management to Assist IoT Application Flows in Home Broadband Networks

Internet of Things (IoT) applications such as telehealth, smart appliances, and smart energy are becoming more common within the home. However, they must compete for bandwidth with traditional applications such as video streaming, video conferencing, and bulk file transfers. Such competition can be detrimental to the IoT applications when home gateways use traditional first-in-first-out (FIFO) queue management. Simply increasing bandwidth between the home gateway and the Internet Service Provider (ISP), even when possible, provides no guarantee of bandwidth for IoT applications since many traditional applications will consume as much bandwidth as is available. In this paper, we explore whether active queue management (AQM), now being implemented in home gateways, can provide protection for IoT flows. We investigate the effect of different AQM algorithms deployed at the home gateway in scenarios with multiple concurrent application flows. We find that deploying multiqueue FlowQueue Controlled Delay (FQ-CoDel) or the hybrid FlowQueue Proportional Integral Controller Enhanced (FQ-PIE) at the home gateway can provide excellent capacity sharing, flow isolation, and good protection in terms of throughput and queuing delays for IoT flows and other applications, which cannot be achieved with traditional FIFO or other single-queue AQMs such as Proportional Integral Controller Enhanced (PIE).

Active queue management in DOCSIS 3.1 networks

An important new feature in the DOCSIS 3.1 specification is active queue management (AQM). AQM provides a solution to the problem of providing good application layer quality of experience when multiple applications share a network connection. The need for AQM arises due to the presence of packet buffering in network elements and the mechanics of the TCP congestion avoidance algorithm. Based on simulated performance and implementation considerations, a variant of the Proportional Integral Controller Enhanced (PIE) algorithm, called DOCSIS-PIE, is now included in DOCSIS 3.1 and has recently been added to the DOCSIS 3.0 specification as well. Implementation of DOCSIS-PIE is mandatory for implementation in DOCSIS 3.1 cable modems, and recommended for implementation in DOCSIS 3.0 cable modems. In addition to the mandatory/ recommended algorithm, DOCSIS 3.1 and 3.0 cable modem vendors are free to support additional AQM algorithms of their choosing. For managing downstream traffic, the DOCSIS 3.1 specification mandates that the CMTS support an AQM technology, but does not require a specific algorithm. This article discusses the underlying problem that is addressed by AQM, the selection of DOCSIS-PIE as the algorithm of choice for DOCSIS 3.1 cable modems, and the expected performance of the algorithm in simulated network conditions.